Before we start learning about the ggpubr package, let’s remember a
few key points about data visualization.
1. Different graphs emphasize different aspects of the data:
2. Why you should not use a bar graph:
3. Don’t conceal your data:
- Seven distributions of data, shown as raw data points (of
strip-plots), as box plots, and as violin plots.
- Box-plots are commonly used to show the distribution of a dataset,
and are better than simply showing the mean or median value. However,
here we can see as the distribution of points changes, the box-plot
remains static.
- Violin plots are good for presenting distribution of your data
with more detail than it is available with a box plot.
4. Allow your PI/colleagues/readers to evaluate your data
effectively:
- Jittering is useful when you have a discrete position and a
relatively small number of points.
- Do not use jitter when the exact location of a
mark is important to the analysis.
ggplot2 is a popular data visualization package developed by Hadley
Wickham for programming langugage R
You can create highly customizable plots using ggplot2 package
Grammar of ggplot2
ggplot function accepts two arguments
- Dataset to be plotted (data.frame format)
- Aesthetic mappings provided by aes function
Additional parameters such as geometric objects (e.g. points, lines,
bars) are passed on by appending them with + as separator.
Let’s familiarize ourselves with ggplot2 grammar:
# We are using data from the National Morbidity and Mortality Air Pollution Study (NMMAPS).
# To make the plots manageable we are limiting the data to Chicago and 1997-2000.
chicago <- readr::read_csv("https://cedricscherer.com/data/chicago-nmmaps-custom.csv")
#list top 10 rows
head(chicago, 10)
ggplot(chicago, aes(x = date, y = temp))
# Hmm, only a white panel? Why? This is because {ggplot2} does not know how we want to plot that data! Do we want a line plot, a scatter plot or something else?
ggplot(chicago, aes(x = date, y = temp)) + geom_point()
# This gives us a simple scatter plot but let's get more creative in the next example
# Let's customize our plot by adding a title, subtitle, caption, tag and changing the title of our legend
ggplot(chicago, aes(x = date, y = temp, color = season)) +
geom_point() +
labs(x = "Year", y = "Temperature (?F)",
color = "Seasons",
title = "Temperatures in Chicago",
subtitle = "Seasonal pattern of daily temperatures from 1997 to 2001",
caption = "Data: NMMAPS",
tag = "Fig. 1")+
theme_classic() # classic and simple theme
Create publication ready plots with ggpubr
- ggplot2 package is excellent and flexible for elegant data
visualization in R.
- However the default generated plots requires some formatting
before they can be used in publications.
In the following code chunk, we will extract the mRNA expression for
five genes of interest - GATA3, PTEN, XBP1, ESR1 and MUC1 - from 3
different data sets:
- Breast invasive carcinoma (BRCA),
- Ovarian serous cystadenocarcinoma (OV)
- Lung squamous cell carcinoma (LUSC)
expr <- expressionsTCGA(BRCA.mRNA, OV.mRNA, LUSC.mRNA, extract.cols = c("GATA3", "PTEN", "XBP1","ESR1", "MUC1"))
# We can simplify data set names by removing the ".mRNA" tag. This can be done using the R base function gsub().
expr$dataset <- gsub(pattern = ".mRNA", replacement = "", expr$dataset)
my_comparisons <- list(c("BRCA", "OV"), c("OV", "LUSC")) # add the comparisons you want to make
# ggboxplot function creates a box plot. Here we are using the expression data from the TCGA package. We are telling the function to use expression values for GATA3 gene from different cancer types (Dataset column forming the x axis) while filling data points based on cancer type (dataset column) based on the color palette of New England Journal of Medicine (NEJM) that we obtain through ggsci package.
ggboxplot(expr, x = "dataset", y = "GATA3", title = "GATA3", ylab = "Expression", fill = "dataset", palette = "nejm")+ stat_compare_means(comparisons = my_comparisons)
Instead of repeating the same R code for each gene, you can create a
list of plots at once, as follow:
# Create a list of plots
my_comparisons <- list(c("BRCA", "OV"), c("OV", "LUSC"))
p <- ggboxplot(expr, x = "dataset",
y = c("GATA3", "PTEN", "XBP1"),
title = c("GATA3", "PTEN", "XBP1"),
ylab = "Expression",
fill = "dataset", palette = "nejm")
# View GATA3
p$GATA3+
stat_compare_means(comparisons = my_comparisons)
# View PTEN
p$PTEN+
stat_compare_means(comparisons = my_comparisons)
# View XBP1
p$XBP1+
stat_compare_means(comparisons = my_comparisons)
Please note that when the argument y contains multiple variables (in
this case multiple gene names), then the other arguments such as title,
xlab and ylab can be also a character vector of same length as y.
You can merge the 3 plots using the argument merge =
TRUE:
ggviolin(expr, x = "dataset",
y = c("GATA3", "PTEN", "XBP1"),
merge = TRUE,
ylab = "Expression",
palette = "igv",
add = c("dotplot", "jitter"),
add.params = list(size = 0.9, binwidth = 0.08, alpha = 0.2))
# use add.params argument to add parameters (color, alpha, size, binwidth) for the argument 'add'
To combine the three gene expression plots into a multi-panel plot,
use the argument combine = TRUE:
# Let's try to make a first draft figure to show your colleague or your PI
my_comparisons <- list(c("BRCA", "OV"), c("OV", "LUSC"))
boxp <- ggboxplot(expr, x = "dataset",
y = c("GATA3", "PTEN", "XBP1"),
combine = TRUE,
ylab = "Expression",
fill = "dataset", palette = "nejm")+
stat_compare_means(comparisons = my_comparisons, label = "p.signif", method="t.test")
ggpar(boxp, ylim = c(-8,8)) # Let's change the y-axis limits using ggpar function of ggpubr to make the significance test results fit better
